Direct electrochemical bacterial sensor using ZnO nanorods disposable electrode

Purpose: This paper aims to report a prototype of a reliable method for rapid, sensitive bacterial detection by using a low-cost zinc oxide nanorods (ZnONRs)-based electrochemical sensor. Design/methodology/approach: The ZnONRs have been grown on the surface of a disposable, miniaturized working electrode (WE) using the low-temperature hydrothermal technique. Scanning electron microscopy and energy dispersion spectroscopy have been performed to characterize the distribution as well as the chemical composition of the ZnONRs on the surface, respectively. Moreover, the cyclic voltammetry test has been implemented to assess the effect of the ZnONRs on the signal conductivity between −1 V and 1 V with a scan rate of 0.01 V/s. Likewise, the effect of using different bacterial concentrations in phosphate-buffered saline has been investigated. Findings: The morphological characterization has shown a highly distributed ZnONR on the WE with uneven alignment. Also, the achieved response time was about 12 minutes and the lower limit of detection was approximately 103 CFU abbreviation for Colony Forming Unit/mL. Originality/value: This paper illustrates an outcome of an experimental work on a ZnONRs-based electrochemical biosensor for direct detection of bacteria.</p

Depression Identification from Gait Spectrum Features Based on Hilbert-Huang Transform

Depression is a common mental illness, which is extremely harmful to individuals and society. Timely and effective diagnosis is very important for patients&rsquo; treatments and depression preventions. In this paper, we treat the trajectory of gait as signal, proposing a new direction to detect the depression with gait frequency features based on Hilbert-Huang transform (HHT). Two groups of participants are recruited in this experiment, including 47 healthy people and 54 depressed patients, respectively. We process the gait data with HHT and build the classification models which verification method is leave-one-out. The best result of our work is 91.09% when the model I is adopted and the classifier is SVM. The corresponding specificity and sensitivity are 87.23% and 94.44% respectively. It verifies that the gait frequency of patients with depression is significantly different from that of healthy people, and the frequency domain features of gait are helpful for the diagnosis of depression. &copy; 2019, Springer Nature Switzerland AG.</p